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Automatic Biological Cell Counting Using a Modified Gradient Hough Transform

Published online by Cambridge University Press:  01 February 2017

Emmanuel Denimal ,
Ambroise Marin ,
Stéphane Guyot ,
Ludovic Journaux  and
Paul Molin
Emmanuel Denimal*
Affiliation:
AgroSup Dijon, Université Bourgogne Franche-Comté, PAM UMR A 02.102, F-21000 Dijon, France
Ambroise Marin
Affiliation:
AgroSup Dijon, Université Bourgogne Franche-Comté, PAM UMR A 02.102, F-21000 Dijon, France
Stéphane Guyot
Affiliation:
AgroSup Dijon, Université Bourgogne Franche-Comté, PAM UMR A 02.102, F-21000 Dijon, France
Ludovic Journaux
Affiliation:
AgroSup Dijon, Université Bourgogne Franche-Comté, PAM UMR A 02.102, F-21000 Dijon, France AgrosupDijon, Université Bourgogne Franche-Comté, Le2i FRE2005 F-21000 Dijon, France
Paul Molin
Affiliation:
AgroSup Dijon, Université Bourgogne Franche-Comté, PAM UMR A 02.102, F-21000 Dijon, France
*
*Corresponding author. emmanuel.denimal@agrosupdijon.fr
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Abstract

We present a computational method for pseudo-circular object detection and quantitative characterization in digital images, using the gradient accumulation matrix as a basic tool. This Gradient Accumulation Transform (GAT) was first introduced in 1992 by Kierkegaard and recently used by Kaytanli & Valentine. In the present article, we modify the approach by using the phase coding studied by Cicconet, and by adding a “local contributor list” (LCL) as well as a “used contributor matrix” (UCM), which allow for accurate peak detection and exploitation. These changes help make the GAT algorithm a robust and precise method to automatically detect pseudo-circular objects in a microscopic image. We then present an application of the method to cell counting in microbiological images.

Keywords

microscopycellcountinghoughcircle
Type
Instrumentation and Software
Information
Microscopy and Microanalysis , Volume 23 , Issue 1 , February 2017 , pp. 11 - 21
Copyright
© Microscopy Society of America 2017 

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